Updated Analysis of C:B Ratio Over Time
Loading Libraries:
library(mgcv)## Loading required package: nlme## This is mgcv 1.8-40. For overview type 'help("mgcv-package")'.library(here)## here() starts at C:/Users/hensl/Downloads/NOAA IN FISH! Internship/IN FISH Woods Hole Projectlibrary(dplyr)##
## Attaching package: 'dplyr'## The following object is masked from 'package:nlme':
##
## collapse## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(ggpubr)Loading In Data:
setwd("C:/Users/hensl/Downloads/NOAA IN FISH! Internship/IN FISH Woods Hole Project")
data_comb <- read.csv("GAM Data/updated cb ratios.csv")Converting the species codes (svspp) to common names, just for ease of reference for myself:
data_comb$svspp[data_comb$svspp == 15] <- 'Spiny.Dogfish'
data_comb$svspp[data_comb$svspp == 23] <- 'Winter.Skate'
data_comb$svspp[data_comb$svspp == 75] <- 'Pollock'
data_comb$svspp[data_comb$svspp == 76] <- 'White.Hake'
data_comb$svspp[data_comb$svspp == 77] <- 'Red.Hake'
data_comb$svspp[data_comb$svspp == 197] <- 'Goosefish'
data_comb$svspp[data_comb$svspp == 28] <- 'Thorny.Skate'
data_comb$svspp[data_comb$svspp == 73] <- 'Atlantic.Cod'
data_comb$svspp[data_comb$svspp == 164] <- 'Sea.Raven'
data_comb$svspp[data_comb$svspp == 72] <- 'Silver.Hake'
data_comb$svspp[data_comb$svspp == 103] <- 'Summer.Flounder'
data_comb$svspp[data_comb$svspp == 104] <- 'Fourspot.Flounder'
data_comb$svspp[data_comb$svspp == 108] <- 'Windowpane'
data_comb$svspp[data_comb$svspp == 112] <- 'Buckler.Dory'
data_comb$svspp[data_comb$svspp == 135] <- 'Blue.Fish'
data_comb$svspp[data_comb$svspp == 163] <- 'Longhorn.Sculpin'
data_comb$svspp[data_comb$svspp == 172] <- 'Striped.Searobin'Filtering the data to only show “all” prey types, so I can analyze total consumption:
data_comb_f <- filter(data_comb, prey == 'all')Removing NA’s:
data_comb_f <- data_comb_f %>% na.omit(cb)Filtering the dataset to focus only on spring samples:
dc_spring <- filter(data_comb_f, seacat == 'SPRING')Building generalized linear models and generalized additive models broken down by each study species: (For the GLM plots, I used the geom_gls() function from the ecodata package so that trend lines would only appear on the plots if a significant trend was detected. For the GAMs, I built the models to the best fit model (lowest AIC value), and therefore the functional set up of the GAMs between some of the species are different. I did additionally z-score the bottom temperature data when using it in the model, but wasn’t sure if this was appropriate to do, especially since the bottom temperature is incorporated into the model output of consumption:biomass values. In case it was something we explored, I incorporated the information in these models presented below and switched between the z-scored bottom temperature and raw bottom temperature depending on which produced a better model output. I would like to note for this that sometimes the trend depicted via the z-scored bottom temperature versus the raw bottom temperature was vastly different. Please let me know your thoughts!)
## Spiny Dogfish
### GLM
dc_spring_s.d <- filter(dc_spring, svspp == 'Spiny.Dogfish')
s.d_plot <- ggplot(data = dc_spring_s.d, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.01, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Spiny Dogfish")
s.d_plot
### GAM
dc_spring_s.d <- filter(dc_spring, svspp == 'Spiny.Dogfish')
dc_spring_s.d$z_bt <- (dc_spring_s.d$bt - mean(dc_spring_s.d$bt))/(dc_spring_s.d$bt_sd)
mod1 <- gam(cb ~ s(year) + s(bt), data=dc_spring_s.d, family = 'gaussian')
summary(mod1)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20015 0.01754 11.41 3.15e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 2.493 3.103 4.172 0.0113 *
## s(bt) 1.000 1.000 3.122 0.0847 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.224 Deviance explained = 28.6%
## GCV = 0.015381 Scale est. = 0.013845 n = 45mod1$aic## [1] -58.63464plot(mod1, pages=1, scheme=1, unconditional=TRUE, main = "Spiny Dogfish")## Warning in plot.gam(mod1, pages = 1, scheme = 1, unconditional = TRUE, main =
## "Spiny Dogfish"): Smoothness uncertainty corrected covariance not available
gam.check(mod1)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 11 iterations.
## The RMS GCV score gradient at convergence was 1.078509e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 2.49 1.00 0.44
## s(bt) 9.00 1.00 1.09 0.66## Winter Skate
### GLM
dc_spring_w.s <- filter(dc_spring, svspp == 'Winter.Skate')
w.s_plot <- ggplot(data = dc_spring_w.s,aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.01, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Winter Skate")
w.s_plot
### GAM
dc_spring_w.s <- filter(dc_spring, svspp == 'Winter.Skate')
dc_spring_w.s$z_bt <- (dc_spring_w.s$bt - mean(dc_spring_w.s$bt))/(dc_spring_w.s$bt_sd)
mod2 <- gam(cb ~ s(year) + s(bt), data=dc_spring_w.s, family = 'gaussian')
summary(mod2)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.17202 0.01246 13.81 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 2.778 3.468 6.214 0.000999 ***
## s(bt) 1.702 2.099 0.579 0.618832
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.335 Deviance explained = 40.4%
## GCV = 0.0077977 Scale est. = 0.0068266 n = 44mod2$aic## [1] -87.45114plot(mod2, pages=1, scheme=1, unconditional=TRUE, main = "Winter Skate")## Warning in plot.gam(mod2, pages = 1, scheme = 1, unconditional = TRUE, main =
## "Winter Skate"): Smoothness uncertainty corrected covariance not available
gam.check(mod2)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 4 iterations.
## The RMS GCV score gradient at convergence was 4.815412e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 2.78 1.00 0.42
## s(bt) 9.00 1.70 1.07 0.62## Pollock
### GLM
dc_spring_p <- filter(dc_spring, svspp == 'Pollock')
p_plot <- ggplot(data = dc_spring_p, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.01, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Pollock")
p_plot
### GAM
dc_spring_p <- filter(dc_spring, svspp == 'Pollock')
dc_spring_p$z_bt <- (dc_spring_p$bt - mean(dc_spring_p$bt))/(dc_spring_p$bt_sd)
mod3 <- gam(cb ~ s(year) + s(bt), data=dc_spring_p, family = 'gaussian')
summary(mod3)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.27819 0.03223 8.632 8.69e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1.210 1.387 3.008 0.126
## s(bt) 3.543 4.395 1.777 0.160
##
## R-sq.(adj) = 0.195 Deviance explained = 27.8%
## GCV = 0.055624 Scale est. = 0.048815 n = 47mod3$aic## [1] -1.176771plot(mod3, pages=1, scheme=1, unconditional=TRUE, main = "Pollock")## Warning in plot.gam(mod3, pages = 1, scheme = 1, unconditional = TRUE, main =
## "Pollock"): Smoothness uncertainty corrected covariance not available
gam.check(mod3)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 23 iterations.
## The RMS GCV score gradient at convergence was 8.334921e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 1.21 1.00 0.47
## s(bt) 9.00 3.54 1.16 0.85## White Hake
### GLM
dc_spring_w.h <- filter(dc_spring, svspp == 'White.Hake')
w.h_plot <- ggplot(data = dc_spring_w.h, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.01, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("White Hake")
w.h_plot
### GAM
dc_spring_w.h <- filter(dc_spring, svspp == 'White.Hake')
dc_spring_w.h$z_bt <- (dc_spring_w.h$bt - mean(dc_spring_w.h$bt))/(dc_spring_w.h$bt_sd)
mod4 <- gam(cb ~ s(year) + s(bt), data=dc_spring_w.h, family = 'gaussian')
summary(mod4)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.6946 0.1131 6.142 4.36e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1.000 1.000 0.030 0.8630
## s(bt) 8.735 8.978 3.004 0.0101 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.298 Deviance explained = 44.7%
## GCV = 0.77902 Scale est. = 0.60109 n = 47mod4$aic## [1] 120.74plot(mod4, pages=1, scheme=1, unconditional=TRUE, main = "White Hake")## Warning in plot.gam(mod4, pages = 1, scheme = 1, unconditional = TRUE, main =
## "White Hake"): Smoothness uncertainty corrected covariance not available
gam.check(mod4)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 16 iterations.
## The RMS GCV score gradient at convergence was 1.598817e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 1.00 0.96 0.30
## s(bt) 9.00 8.73 1.46 0.99## Red Hake
### GLM
dc_spring_r.h <- filter(dc_spring, svspp == 'Red.Hake')
r.h_plot <- ggplot(data = dc_spring_r.h, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.01, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Red Hake")
r.h_plot
### GAM
dc_spring_r.h <- filter(dc_spring, svspp == 'Red.Hake')
dc_spring_r.h$z_bt <- (dc_spring_r.h$bt - mean(dc_spring_r.h$bt))/(dc_spring_r.h$bt_sd)
mod5 <- gam(cb ~ s(year) + s(bt), data=dc_spring_r.h, family = 'gaussian')
summary(mod5)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.4836 0.1033 4.681 2.99e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 4.497 5.509 1.442 0.233
## s(bt) 1.575 1.938 0.912 0.467
##
## R-sq.(adj) = 0.11 Deviance explained = 22.2%
## GCV = 0.61135 Scale est. = 0.52311 n = 49mod5$aic## [1] 115.8128plot(mod5, pages=1, scheme=1, unconditional=TRUE, main = "Red Hake")## Warning in plot.gam(mod5, pages = 1, scheme = 1, unconditional = TRUE, main =
## "Red Hake"): Smoothness uncertainty corrected covariance not available
gam.check(mod5)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 6 iterations.
## The RMS GCV score gradient at convergence was 1.143809e-05 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 4.50 1.20 0.98
## s(bt) 9.00 1.57 0.92 0.12## Goosefish
### GLM
dc_spring_g <- filter(dc_spring, svspp == 'Goosefish')
g_plot <- ggplot(data = dc_spring_g, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.01, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Goosefish")
g_plot
### GAM
dc_spring_g <- filter(dc_spring, svspp == 'Goosefish')
dc_spring_g$z_bt <- (dc_spring_g$bt - mean(dc_spring_g$bt))/(dc_spring_g$bt_sd)
mod6 <- gam(cb ~ s(year) + s(bt), data=dc_spring_g, family = 'gaussian')
summary(mod6)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.65685 0.03935 16.69 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 7.68 8.558 4.387 0.000821 ***
## s(bt) 1.00 1.000 0.082 0.776613
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.444 Deviance explained = 55.4%
## GCV = 0.088772 Scale est. = 0.069677 n = 45mod6$aic## [1] 18.28955plot(mod6, pages=1, scheme=1, unconditional=TRUE, main = "Goosefish")## Warning in plot.gam(mod6, pages = 1, scheme = 1, unconditional = TRUE, main =
## "Goosefish"): Smoothness uncertainty corrected covariance not available
gam.check(mod6)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 15 iterations.
## The RMS GCV score gradient at convergence was 6.951738e-08 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 7.68 1.29 0.96
## s(bt) 9.00 1.00 0.95 0.34## Thorny Skate
### GLM
dc_spring_t.s <- filter(dc_spring, svspp == 'Thorny.Skate')
t.s_plot <- ggplot(data = dc_spring_t.s, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Thorny Skate")
t.s_plot
### GAM
dc_spring_t.s <- filter(dc_spring, svspp == 'Thorny.Skate')
dc_spring_t.s$z_bt <- (dc_spring_t.s$bt - mean(dc_spring_t.s$bt))/(dc_spring_t.s$bt_sd)
mod7 <- gam(cb ~ s(year) + s(bt), data=dc_spring_t.s, family = 'gaussian')
summary(mod7)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.23653 0.02141 11.05 9.69e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 5.487 6.584 2.418 0.0442 *
## s(bt) 1.831 2.296 3.991 0.0195 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.496 Deviance explained = 58.6%
## GCV = 0.024008 Scale est. = 0.019254 n = 42mod7$aic## [1] -37.34534plot(mod7, pages=1, scheme=1, unconditional=TRUE, main = "Thorny Skate")## Warning in plot.gam(mod7, pages = 1, scheme = 1, unconditional = TRUE, main =
## "Thorny Skate"): Smoothness uncertainty corrected covariance not available
gam.check(mod7)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 6 iterations.
## The RMS GCV score gradient at convergence was 6.747403e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 5.49 1.01 0.42
## s(bt) 9.00 1.83 1.43 1.00## Atlantic Cod
### GLM
dc_spring_a.c <- filter(dc_spring, svspp == 'Atlantic.Cod')
a.c_plot <- ggplot(data = dc_spring_a.c,aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Atlantic Cod")
a.c_plot
### GAM
dc_spring_a.c <- filter(dc_spring, svspp == 'Atlantic.Cod')
dc_spring_a.c$z_bt <- (dc_spring_a.c$bt - mean(dc_spring_a.c$bt))/(dc_spring_a.c$bt_sd)
mod8 <- gam(cb ~ s(year) + s(bt), data=dc_spring_a.c, family = 'gaussian')
summary(mod8)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.44934 0.02868 15.66 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1.00 1.000 1.569 0.2173
## s(bt) 2.53 3.183 4.162 0.0104 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.291 Deviance explained = 34.6%
## GCV = 0.042795 Scale est. = 0.03867 n = 47mod8$aic## [1] -13.19981plot(mod8, pages=1, scheme=1, unconditional=TRUE, main = "Atlantic Cod")## Warning in plot.gam(mod8, pages = 1, scheme = 1, unconditional = TRUE, main =
## "Atlantic Cod"): Smoothness uncertainty corrected covariance not available
gam.check(mod8)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 13 iterations.
## The RMS GCV score gradient at convergence was 5.122689e-08 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 1.00 1.32 0.99
## s(bt) 9.00 2.53 1.03 0.46## Sea Raven
### GLM
dc_spring_s.r <- filter(dc_spring, svspp == 'Sea.Raven')
s.r_plot <- ggplot(data = dc_spring_s.r, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Sea Raven")
s.r_plot
### GAM
dc_spring_s.r <- filter(dc_spring, svspp == 'Sea.Raven')
dc_spring_s.r$z_bt <- (dc_spring_s.r$bt - mean(dc_spring_s.r$bt))/(dc_spring_s.r$bt_sd)
mod9 <- gam(cb ~ s(year) + s(bt), data=dc_spring_s.r, family = 'gaussian')
summary(mod9)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.57923 0.04025 14.39 2.53e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 5.76 6.912 4.577 0.00119 **
## s(bt) 1.00 1.000 3.414 0.07311 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.537 Deviance explained = 61.2%
## GCV = 0.085001 Scale est. = 0.069661 n = 43mod9$aic## [1] 16.43412plot(mod9, pages=1, scheme=1, unconditional=TRUE, main = "Sea Raven")## Warning in plot.gam(mod9, pages = 1, scheme = 1, unconditional = TRUE, main =
## "Sea Raven"): Smoothness uncertainty corrected covariance not available
gam.check(mod9)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 10 iterations.
## The RMS GCV score gradient at convergence was 1.08166e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 5.76 1.25 0.94
## s(bt) 9.00 1.00 1.22 0.92## Silver Hake
### GLM
dc_spring_s.h <- filter(dc_spring, svspp == 'Silver.Hake')
s.h_plot <- ggplot(data = dc_spring_s.h, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Silver Hake")
s.h_plot
### GAM
dc_spring_s.h <- filter(dc_spring, svspp == 'Silver.Hake')
dc_spring_s.h$z_bt <- (dc_spring_s.h$bt - mean(dc_spring_s.h$bt))/(dc_spring_s.h$bt_sd)
mod10 <- gam(cb ~ s(year) + s(bt), data=dc_spring_s.h, family = 'gaussian')
summary(mod10)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.68063 0.06495 10.48 9.59e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1.00 1.000 1.536 0.223
## s(bt) 8.15 8.792 2.416 0.038 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.242 Deviance explained = 38.9%
## GCV = 0.25678 Scale est. = 0.20248 n = 48mod10$aic## [1] 70.45327plot(mod10, pages=1, scheme=1, unconditional=TRUE, main = "Silver Hake")## Warning in plot.gam(mod10, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod10)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 13 iterations.
## The RMS GCV score gradient at convergence was 9.120739e-08 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 1.00 0.96 0.39
## s(bt) 9.00 8.15 1.04 0.56## Summer Flounder
### GLM
dc_spring_s.f <- filter(dc_spring, svspp == 'Summer.Flounder')
s.f_plot <- ggplot(data = dc_spring_s.f, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Summer Flounder")
s.f_plot
### GAM
dc_spring_s.f <- filter(dc_spring, svspp == 'Summer.Flounder')
dc_spring_s.f$z_bt <- (dc_spring_s.f$bt - mean(dc_spring_s.f$bt))/(dc_spring_s.f$bt_sd)
mod11 <- gam(cb ~ s(year) + s(bt), data=dc_spring_s.f, family = 'gaussian')
summary(mod11)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16049 0.01912 8.395 1.58e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1 1 1.029 0.316
## s(bt) 1 1 1.088 0.303
##
## R-sq.(adj) = -0.00212 Deviance explained = 4.34%
## GCV = 0.017621 Scale est. = 0.016446 n = 45mod11$aic## [1] -52.24465plot(mod11, pages=1, scheme=1, unconditional=TRUE, main = "Summer Flounder")## Warning in plot.gam(mod11, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod11)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 12 iterations.
## The RMS GCV score gradient at convergence was 3.793624e-08 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9 1 0.98 0.35
## s(bt) 9 1 1.21 0.90## Fourspot Flounder
### GLM
dc_spring_f.f <- filter(dc_spring, svspp == 'Fourspot.Flounder')
f.f_plot <- ggplot(data = dc_spring_f.f, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Fourspot Flounder")
f.f_plot
### GAM
dc_spring_f.f <- filter(dc_spring, svspp == 'Fourspot.Flounder')
dc_spring_f.f$z_bt <- (dc_spring_f.f$bt - mean(dc_spring_f.f$bt))/(dc_spring_f.f$bt_sd)
mod12 <- gam(cb ~ s(year) + s(bt), data=dc_spring_f.f, family = 'gaussian')
summary(mod12)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.12578 0.01143 11 3.78e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 6.101 7.236 4.636 0.000749 ***
## s(bt) 2.360 2.943 2.679 0.072895 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.481 Deviance explained = 57.8%
## GCV = 0.0075719 Scale est. = 0.0060146 n = 46mod12$aic## [1] -94.35158plot(mod12, pages=1, scheme=1, unconditional=TRUE, main = "Fourspot Flounder")## Warning in plot.gam(mod12, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod12)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 8 iterations.
## The RMS GCV score gradient at convergence was 7.74027e-08 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 6.10 0.99 0.46
## s(bt) 9.00 2.36 0.98 0.38## Windowpane
### GLM
dc_spring_w <- filter(dc_spring, svspp == 'Windowpane')
w_plot <- ggplot(data = dc_spring_w, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Windowpane")
w_plot
### GAM
dc_spring_w <- filter(dc_spring, svspp == 'Windowpane')
dc_spring_w$z_bt <- (dc_spring_w$bt - mean(dc_spring_w$bt))/(dc_spring_w$bt_sd)
mod13 <- gam(cb ~ s(year) + s(bt), data=dc_spring_w, family = 'gaussian')
summary(mod13)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.20945 0.02312 9.061 2.44e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1 1 0.694 0.409
## s(bt) 1 1 0.388 0.537
##
## R-sq.(adj) = -0.00309 Deviance explained = 4.36%
## GCV = 0.02523 Scale est. = 0.02351 n = 44mod13$aic## [1] -35.25549plot(mod13, pages=1, scheme=1, unconditional=TRUE, main = "Windowpane")## Warning in plot.gam(mod13, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod13)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 12 iterations.
## The RMS GCV score gradient at convergence was 3.800415e-08 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9 1 1.28 0.98
## s(bt) 9 1 1.07 0.58## Buckler Dory
### GLM
dc_spring_b.d <- filter(dc_spring, svspp == 'Buckler.Dory')
b.d_plot <- ggplot(data = dc_spring_b.d, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Buckler Dory")
b.d_plot
### GAM
dc_spring_b.d <- filter(dc_spring, svspp == 'Buckler.Dory')
dc_spring_b.d$z_bt <- (dc_spring_b.d$bt - mean(dc_spring_b.d$bt))/(dc_spring_b.d$bt_sd)
mod14 <- gam(cb ~ s(year) , data=dc_spring_b.d, family = 'gaussian')
mod14.a <- gam(cb ~ s(bt) , data=dc_spring_b.d, family = 'gaussian')
summary(mod14)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2483 0.1077 2.306 0.0351 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1.257 1.464 0.162 0.875
##
## R-sq.(adj) = -0.0397 Deviance explained = 3.72%
## GCV = 0.23872 Scale est. = 0.2088 n = 18summary(mod14.a)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.2483 0.0812 3.058 0.00887 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(bt) 3.585 4.378 2.851 0.0662 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.409 Deviance explained = 53.4%
## GCV = 0.15923 Scale est. = 0.11867 n = 18mod14$aic## [1] 26.98878mod14.a$aic## [1] 18.59422plot(mod14, pages=1, scheme=1, unconditional=TRUE, main = "Buckler Dory")## Warning in plot.gam(mod14, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
plot(mod14.a, pages=1, scheme=1, unconditional=TRUE, main = "Buckler Dory")## Warning in plot.gam(mod14.a, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod14)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 6 iterations.
## The RMS GCV score gradient at convergence was 1.51097e-06 .
## The Hessian was positive definite.
## Model rank = 10 / 10
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 1.26 1.01 0.34gam.check(mod14.a)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 4 iterations.
## The RMS GCV score gradient at convergence was 2.394827e-06 .
## The Hessian was positive definite.
## Model rank = 10 / 10
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(bt) 9.00 3.58 1.53 0.99## Blue Fish
### GLM
dc_spring_b.f <- filter(dc_spring, svspp == 'Blue.Fish')
b.f_plot <- ggplot(data = dc_spring_b.f, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Blue Fish")
b.f_plot
### GAM
dc_spring_b.f <- filter(dc_spring, svspp == 'Blue.Fish')
dc_spring_b.f$z_bt <- (dc_spring_b.f$bt - mean(dc_spring_b.f$bt))/(dc_spring_b.f$bt_sd)
mod15 <- gam(cb ~ s(year) + s(bt), data=dc_spring_b.f, family = 'gaussian')
summary(mod15)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.007104 0.007321 0.97 0.34
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1 1 0.136 0.715
## s(bt) 1 1 0.052 0.822
##
## R-sq.(adj) = -0.0618 Deviance explained = 0.669%
## GCV = 0.0018924 Scale est. = 0.0017149 n = 32mod15$aic## [1] -108.126plot(mod15, pages=1, scheme=1, unconditional=TRUE, main = "Blue Fish")## Warning in plot.gam(mod15, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod15)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 10 iterations.
## The RMS GCV score gradient at convergence was 1.13215e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9 1 1.08 0.80
## s(bt) 9 1 1.06 0.43## Longhorn Sculpin
### GLM
dc_spring_l.s <- filter(dc_spring, svspp == 'Longhorn.Sculpin')
l.s_plot <- ggplot(data = dc_spring_l.s, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Longhorn Sculpin")
l.s_plot
### GAM
dc_spring_l.s <- filter(dc_spring, svspp == 'Longhorn.Sculpin')
dc_spring_l.s$z_bt <- (dc_spring_l.s$bt - mean(dc_spring_l.s$bt))/(dc_spring_l.s$bt_sd)
mod16 <- gam(cb ~ s(year) + s(z_bt), data=dc_spring_l.s, family = 'gaussian')
summary(mod16)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(z_bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.55097 0.04207 13.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1.230 1.425 24.466 1.69e-05 ***
## s(z_bt) 1.594 1.989 0.606 0.531
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.415 Deviance explained = 45.2%
## GCV = 0.08879 Scale est. = 0.081408 n = 46mod16$aic## [1] 20.81721plot(mod16, pages=1, scheme=1, unconditional=TRUE, main = "Longhorn Sculpin")## Warning in plot.gam(mod16, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod16)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 7 iterations.
## The RMS GCV score gradient at convergence was 1.804805e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 1.23 0.99 0.42
## s(z_bt) 9.00 1.59 1.14 0.84## Striped Searobin
### GLM
dc_spring_s.s <- filter(dc_spring, svspp == 'Striped.Searobin')
s.s_plot <- ggplot(data = dc_spring_s.s, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Striped Searobin")
s.s_plot
### GAM
dc_spring_s.s <- filter(dc_spring, svspp == 'Striped.Searobin')
dc_spring_s.s$z_bt <- (dc_spring_s.s$bt - mean(dc_spring_s.s$bt))/(dc_spring_s.s$bt_sd)
mod17 <- gam(cb ~ s(year), data=dc_spring_s.s, family = 'gaussian')
mod17.a <- gam(cb ~ s(z_bt), data=dc_spring_s.s, family = 'gaussian')
summary(mod17)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04502 0.02581 1.745 0.1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1 1 0.035 0.853
##
## R-sq.(adj) = -0.0602 Deviance explained = 0.221%
## GCV = 0.013486 Scale est. = 0.011987 n = 18summary(mod17.a)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(z_bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.04502 0.02575 1.749 0.0995 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(z_bt) 1 1 0.11 0.745
##
## R-sq.(adj) = -0.0553 Deviance explained = 0.68%
## GCV = 0.013424 Scale est. = 0.011932 n = 18mod17$aic## [1] -24.66844mod17.a$aic## [1] -24.75151plot(mod17, pages=1, scheme=1, unconditional=TRUE, main = "Striped Searobin")## Warning in plot.gam(mod17, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
plot(mod17.a, pages=1, scheme=1, unconditional=TRUE, main = "Striped Searobin")## Warning in plot.gam(mod17.a, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod17)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 10 iterations.
## The RMS GCV score gradient at convergence was 8.748969e-08 .
## The Hessian was positive definite.
## Model rank = 10 / 10
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9 1 1.17 0.58gam.check(mod17.a)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 12 iterations.
## The RMS GCV score gradient at convergence was 1.388623e-07 .
## The Hessian was positive definite.
## Model rank = 10 / 10
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(z_bt) 9 1 1.12 0.49Combining all of the GLMs produced above into one plot for comparison purposes:
glm_cb <- ggarrange(s.d_plot, w.s_plot, p_plot, w.h_plot, r.h_plot, g_plot, t.s_plot, a.c_plot, s.r_plot, s.h_plot,
s.f_plot, f.f_plot, w_plot, b.d_plot, b.f_plot, l.s_plot, s.s_plot, ncol = 4, nrow = 5)
glm_cb
Comparing GAMs side by side for all species of trends in consumption:biomass ratios: (For this, I broke the analysis up between looking at trends in c:b over time, and then trends in c:b in relation to bottom temperature for the purpose of allowing for a direct comparison between study species. Additionally, I did this to allow us to hone in on just how c:b is changing over time if we decide that including bottom temperature in the model is not appropriate).
# GAM of year v. cb --> I save each of these figures (total of 2 figures) once the matrix of 3x3 is filled in order to have two large figures that I can compare all species with
par(mfrow=c(3,3))
mod1 <- gam(cb ~ s(year), data=dc_spring_s.d, family = 'gaussian')
plot(mod1, scheme=1, unconditional=TRUE, main = "Spiny Dogfish") ## Warning in plot.gam(mod1, scheme = 1, unconditional = TRUE, main = "Spiny
## Dogfish"): Smoothness uncertainty corrected covariance not availablemod2 <- gam(cb ~ s(year), data=dc_spring_w.s, family = 'gaussian')
plot(mod2, scheme=1, unconditional=TRUE, main = "Winter Skate") ## Warning in plot.gam(mod2, scheme = 1, unconditional = TRUE, main = "Winter
## Skate"): Smoothness uncertainty corrected covariance not availablemod3 <- gam(cb ~ s(year), data=dc_spring_p, family = 'gaussian')
plot(mod3, scheme=1, unconditional=TRUE, main = "Pollock") ## Warning in plot.gam(mod3, scheme = 1, unconditional = TRUE, main = "Pollock"):
## Smoothness uncertainty corrected covariance not availablemod4 <- gam(cb ~ s(year), data=dc_spring_w.h, family = 'gaussian')
plot(mod4, scheme=1, unconditional=TRUE, main = "White Hake")## Warning in plot.gam(mod4, scheme = 1, unconditional = TRUE, main = "White
## Hake"): Smoothness uncertainty corrected covariance not availablemod5 <- gam(cb ~ s(year), data=dc_spring_r.h, family = 'gaussian')
plot(mod5, scheme=1, unconditional=TRUE, main = "Red Hake")## Warning in plot.gam(mod5, scheme = 1, unconditional = TRUE, main = "Red Hake"):
## Smoothness uncertainty corrected covariance not availablemod6 <- gam(cb ~ s(year), data=dc_spring_g, family = 'gaussian')
plot(mod6, scheme=1, unconditional=TRUE, main = "Goosefish")## Warning in plot.gam(mod6, scheme = 1, unconditional = TRUE, main = "Goosefish"):
## Smoothness uncertainty corrected covariance not availablemod7 <- gam(cb ~ s(year), data=dc_spring_t.s, family = 'gaussian')
plot(mod7, scheme=1, unconditional=TRUE, main = "Thorny Skate")## Warning in plot.gam(mod7, scheme = 1, unconditional = TRUE, main = "Thorny
## Skate"): Smoothness uncertainty corrected covariance not availablemod8 <- gam(cb ~ s(year), data=dc_spring_a.c, family = 'gaussian')
plot(mod8, scheme=1, unconditional=TRUE, main = "Atlantic Cod")## Warning in plot.gam(mod8, scheme = 1, unconditional = TRUE, main = "Atlantic
## Cod"): Smoothness uncertainty corrected covariance not availablemod9 <- gam(cb ~ s(year), data=dc_spring_s.r, family = 'gaussian')
plot(mod9, scheme=1, unconditional=TRUE, main = "Sea Raven")## Warning in plot.gam(mod9, scheme = 1, unconditional = TRUE, main = "Sea Raven"):
## Smoothness uncertainty corrected covariance not available
mod10 <- gam(cb ~ s(year), data=dc_spring_s.h, family = 'gaussian')
plot(mod10, scheme=1, unconditional=TRUE, main = "Silver Hake")## Warning in plot.gam(mod10, scheme = 1, unconditional = TRUE, main = "Silver
## Hake"): Smoothness uncertainty corrected covariance not availablemod11 <- gam(cb ~ s(year), data=dc_spring_s.f, family = 'gaussian')
plot(mod11, scheme=1, unconditional=TRUE, main = "Summer Flounder")## Warning in plot.gam(mod11, scheme = 1, unconditional = TRUE, main = "Summer
## Flounder"): Smoothness uncertainty corrected covariance not availablemod12 <- gam(cb ~ s(year), data=dc_spring_f.f, family = 'gaussian')
plot(mod12, scheme=1, unconditional=TRUE, main = "Fourspot Flounder")## Warning in plot.gam(mod12, scheme = 1, unconditional = TRUE, main = "Fourspot
## Flounder"): Smoothness uncertainty corrected covariance not availablemod13 <- gam(cb ~ s(year), data=dc_spring_w, family = 'gaussian')
plot(mod13, scheme=1, unconditional=TRUE, main = "Windowpane")## Warning in plot.gam(mod13, scheme = 1, unconditional = TRUE, main =
## "Windowpane"): Smoothness uncertainty corrected covariance not availablemod14 <- gam(cb ~ s(year), data=dc_spring_b.d, family = 'gaussian')
plot(mod14, scheme=1, unconditional=TRUE, main = "Buckler Dory")## Warning in plot.gam(mod14, scheme = 1, unconditional = TRUE, main = "Buckler
## Dory"): Smoothness uncertainty corrected covariance not availablemod15 <- gam(cb ~ s(year), data=dc_spring_b.f, family = 'gaussian')
plot(mod15, scheme=1, unconditional=TRUE, main = "Blue Fish")## Warning in plot.gam(mod15, scheme = 1, unconditional = TRUE, main = "Blue
## Fish"): Smoothness uncertainty corrected covariance not availablemod16 <- gam(cb ~ s(year), data=dc_spring_l.s, family = 'gaussian')
plot(mod16, scheme=1, unconditional=TRUE, main = "Longhorn Sculpin")## Warning in plot.gam(mod16, scheme = 1, unconditional = TRUE, main = "Longhorn
## Sculpin"): Smoothness uncertainty corrected covariance not availablemod17 <- gam(cb ~ s(year), data=dc_spring_s.s, family = 'gaussian')
plot(mod17, scheme = 1, unconditional=TRUE, main = "Striped Searobin")## Warning in plot.gam(mod17, scheme = 1, unconditional = TRUE, main = "Striped
## Searobin"): Smoothness uncertainty corrected covariance not available# GAM of bt v. cb --> same note as above
par(mfrow = c(3,3))
mod1 <- gam(cb ~ s(bt), data=dc_spring_s.d, family = 'gaussian')
plot(mod1, scheme=1, unconditional=TRUE, main = "Spiny Dogfish")## Warning in plot.gam(mod1, scheme = 1, unconditional = TRUE, main = "Spiny
## Dogfish"): Smoothness uncertainty corrected covariance not availablemod2 <- gam(cb ~ s(bt), data=dc_spring_w.s, family = 'gaussian')
plot(mod2, scheme=1, unconditional=TRUE, main = "Winter Skate")## Warning in plot.gam(mod2, scheme = 1, unconditional = TRUE, main = "Winter
## Skate"): Smoothness uncertainty corrected covariance not availablemod3 <- gam(cb ~ s(bt), data=dc_spring_p, family = 'gaussian')
plot(mod3, scheme=1, unconditional=TRUE, main = "Pollock")## Warning in plot.gam(mod3, scheme = 1, unconditional = TRUE, main = "Pollock"):
## Smoothness uncertainty corrected covariance not availablemod4 <- gam(cb ~ s(bt), data=dc_spring_w.h, family = 'gaussian')
plot(mod4, scheme=1, unconditional=TRUE, main = "White Hake")## Warning in plot.gam(mod4, scheme = 1, unconditional = TRUE, main = "White
## Hake"): Smoothness uncertainty corrected covariance not availablemod5 <- gam(cb ~ s(bt), data=dc_spring_r.h, family = 'gaussian')
plot(mod5, scheme=1, unconditional=TRUE, main = " Red Hake")## Warning in plot.gam(mod5, scheme = 1, unconditional = TRUE, main = " Red Hake"):
## Smoothness uncertainty corrected covariance not availablemod6 <- gam(cb ~ s(bt), data=dc_spring_g, family = 'gaussian')
plot(mod6, scheme=1, unconditional=TRUE, main = "Goosefish")## Warning in plot.gam(mod6, scheme = 1, unconditional = TRUE, main = "Goosefish"):
## Smoothness uncertainty corrected covariance not availablemod7 <- gam(cb ~ s(bt), data=dc_spring_t.s, family = 'gaussian')
plot(mod7, scheme=1, unconditional=TRUE, main = "Thorny Skate")## Warning in plot.gam(mod7, scheme = 1, unconditional = TRUE, main = "Thorny
## Skate"): Smoothness uncertainty corrected covariance not availablemod8 <- gam(cb ~ s(bt), data=dc_spring_a.c, family = 'gaussian')
plot(mod8, scheme=1, unconditional=TRUE, main = "Atlantic Cod")## Warning in plot.gam(mod8, scheme = 1, unconditional = TRUE, main = "Atlantic
## Cod"): Smoothness uncertainty corrected covariance not availablemod9 <- gam(cb ~ s(bt), data=dc_spring_s.r, family = 'gaussian')
plot(mod9, scheme=1, unconditional=TRUE, main = "Sea Raven")## Warning in plot.gam(mod9, scheme = 1, unconditional = TRUE, main = "Sea Raven"):
## Smoothness uncertainty corrected covariance not available
mod10 <- gam(cb ~ s(bt), data=dc_spring_s.h, family = 'gaussian')
plot(mod10, scheme=1, unconditional=TRUE, main = "Silver Hake")## Warning in plot.gam(mod10, scheme = 1, unconditional = TRUE, main = "Silver
## Hake"): Smoothness uncertainty corrected covariance not availablemod11 <- gam(cb ~ s(bt), data=dc_spring_s.f, family = 'gaussian')
plot(mod11, scheme=1, unconditional=TRUE, main = "Summer Flounder")## Warning in plot.gam(mod11, scheme = 1, unconditional = TRUE, main = "Summer
## Flounder"): Smoothness uncertainty corrected covariance not availablemod12 <- gam(cb ~ s(bt), data=dc_spring_f.f, family = 'gaussian')
plot(mod12, scheme=1, unconditional=TRUE, main = "Fourspot Flounder")## Warning in plot.gam(mod12, scheme = 1, unconditional = TRUE, main = "Fourspot
## Flounder"): Smoothness uncertainty corrected covariance not availablemod13 <- gam(cb ~ s(bt), data=dc_spring_w, family = 'gaussian')
plot(mod13, scheme=1, unconditional=TRUE, main = "Windowpane")## Warning in plot.gam(mod13, scheme = 1, unconditional = TRUE, main =
## "Windowpane"): Smoothness uncertainty corrected covariance not availablemod14 <- gam(cb ~ s(bt) , data=dc_spring_b.d, family = 'gaussian')
plot(mod14, scheme=1, unconditional=TRUE, main = "Buckler Dory")## Warning in plot.gam(mod14, scheme = 1, unconditional = TRUE, main = "Buckler
## Dory"): Smoothness uncertainty corrected covariance not availablemod15 <- gam(cb ~ s(bt), data=dc_spring_b.f, family = 'gaussian')
plot(mod15, scheme=1, unconditional=TRUE, main = "Blue Fish")## Warning in plot.gam(mod15, scheme = 1, unconditional = TRUE, main = "Blue
## Fish"): Smoothness uncertainty corrected covariance not availablemod16 <- gam(cb ~ s(z_bt), data=dc_spring_l.s, family = 'gaussian')
plot(mod16, scheme=1, unconditional=TRUE, main = "Longhorn Sculpin")## Warning in plot.gam(mod16, scheme = 1, unconditional = TRUE, main = "Longhorn
## Sculpin"): Smoothness uncertainty corrected covariance not availablemod17 <- gam(cb ~ s(z_bt), data=dc_spring_s.s, family = 'gaussian')
plot(mod17, scheme = 1, unconditional=TRUE, main = "Striped Searobin")## Warning in plot.gam(mod17, scheme = 1, unconditional = TRUE, main = "Striped
## Searobin"): Smoothness uncertainty corrected covariance not available
Filtering the dataset to focus only on fall samples:
dc_fall <- filter(data_comb_f, seacat == 'FALL')Building GLMs and GAMs for study species across all prey types for fall:
## Spiny Dogfish
### GLM
dc_fall_s.d <- filter(dc_fall, svspp == 'Spiny.Dogfish')
s.d_plot_f <- ggplot(data = dc_fall_s.d, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Spiny Dogfish")
s.d_plot_f
### GAM
dc_fall_s.d <- filter(dc_fall, svspp == 'Spiny.Dogfish')
dc_fall_s.d$z_bt <- (dc_fall_s.d$bt - mean(dc_fall_s.d$bt))/(dc_fall_s.d$bt_sd)
mod18 <- gam(cb ~ s(year) + s(bt), data=dc_fall_s.d, family = 'gaussian')
summary(mod18)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.430 0.055 7.818 3.86e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 7.547 8.467 2.871 0.0172 *
## s(bt) 1.000 1.000 1.921 0.1748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.34 Deviance explained = 47.1%
## GCV = 0.17001 Scale est. = 0.13312 n = 44mod18$aic## [1] 46.47349plot(mod18, pages=1, scheme=1, unconditional=TRUE, main = "Spiny Dogfish")## Warning in plot.gam(mod18, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod18)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 17 iterations.
## The RMS GCV score gradient at convergence was 1.041789e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 7.55 1.08 0.690
## s(bt) 9.00 1.00 0.74 0.025 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Winter Skate
### GLM
dc_fall_w.s <- filter(dc_fall, svspp == 'Winter.Skate')
w.s_plot_f <- ggplot(data = dc_fall_w.s, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Winter Skate")
w.s_plot_f
### GAM
dc_fall_w.s <- filter(dc_fall, svspp == 'Winter.Skate')
dc_fall_w.s$z_bt <- (dc_fall_w.s$bt - mean(dc_fall_w.s$bt))/(dc_fall_w.s$bt_sd)
mod19 <- gam(cb ~ s(year) + s(z_bt), data=dc_fall_w.s, family = 'gaussian')
summary(mod19)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(z_bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37136 0.04394 8.451 1.7e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1.222 1.410 2.468 0.148
## s(z_bt) 2.083 2.632 2.039 0.105
##
## R-sq.(adj) = 0.187 Deviance explained = 24.8%
## GCV = 0.096087 Scale est. = 0.086893 n = 45mod19$aic## [1] 23.85156plot(mod19, pages=1, scheme=1, unconditional=TRUE, main = "Winter Skate")## Warning in plot.gam(mod19, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod19)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 15 iterations.
## The RMS GCV score gradient at convergence was 6.180394e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 1.22 0.96 0.28
## s(z_bt) 9.00 2.08 0.85 0.10## Pollock
### GLM
dc_fall_p <- filter(dc_fall, svspp == 'Pollock')
p_plot_f <- ggplot(data = dc_fall_p, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Pollock")
p_plot_f
### GAM
dc_fall_p <- filter(dc_fall, svspp == 'Pollock')
dc_fall_p$z_bt <- (dc_fall_p$bt - mean(dc_fall_p$bt))/(dc_fall_p$bt_sd)
mod20 <- gam(cb ~ s(year) + s(bt), data=dc_fall_p, family = 'gaussian')
summary(mod20)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.34983 0.06578 5.318 3.75e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 2.941 3.68 3.239 0.0221 *
## s(bt) 1.000 1.00 2.137 0.1513
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.192 Deviance explained = 26.2%
## GCV = 0.22725 Scale est. = 0.20336 n = 47mod20$aic## [1] 65.18195plot(mod20, pages=1, scheme=1, unconditional=TRUE, main = "Pollock")## Warning in plot.gam(mod20, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod20)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 12 iterations.
## The RMS GCV score gradient at convergence was 8.345769e-08 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 2.94 0.86 0.14
## s(bt) 9.00 1.00 1.14 0.85## White Hake
### GLM
dc_fall_w.h <- filter(dc_fall, svspp == 'White.Hake')
w.h_plot_f <- ggplot(data = dc_fall_w.h, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("White Hake")
w.h_plot_f
### GAM
dc_fall_w.h <- filter(dc_fall, svspp == 'White.Hake')
dc_fall_w.h$z_bt <- (dc_fall_w.h$bt - mean(dc_fall_w.h$bt))/(dc_fall_w.h$bt_sd)
mod21 <- gam(cb ~ s(year) + s(z_bt), data=dc_fall_w.h, family = 'gaussian')
summary(mod21)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(z_bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.85838 0.06975 12.31 5.31e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1 1 6.207 0.0165 *
## s(z_bt) 1 1 0.011 0.9178
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0944 Deviance explained = 13.3%
## GCV = 0.24906 Scale est. = 0.23349 n = 48mod21$aic## [1] 71.29863plot(mod21, pages=1, scheme=1, unconditional=TRUE, main = "White Hake")## Warning in plot.gam(mod21, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod21)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 12 iterations.
## The RMS GCV score gradient at convergence was 9.798492e-08 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9 1 1.01 0.47
## s(z_bt) 9 1 1.07 0.57## Red Hake
### GLM
dc_fall_r.h <- filter(dc_fall, svspp == 'Red.Hake')
r.h_plot_f <- ggplot(data = dc_fall_r.h, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Red Hake")
r.h_plot_f
### GAM
dc_fall_r.h <- filter(dc_fall, svspp == 'Red.Hake')
dc_fall_r.h$z_bt <- (dc_fall_r.h$bt - mean(dc_fall_r.h$bt))/(dc_fall_r.h$bt_sd)
mod22 <- gam(cb ~ s(year) + s(bt), data=dc_fall_r.h, family = 'gaussian')
summary(mod22)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0205 0.1039 9.821 2.06e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1.579 1.968 0.794 0.481
## s(bt) 3.703 4.639 1.544 0.206
##
## R-sq.(adj) = 0.179 Deviance explained = 27.1%
## GCV = 0.59623 Scale est. = 0.5182 n = 48mod22$aic## [1] 112.4941plot(mod22, pages=1, scheme=1, unconditional=TRUE, main = "Red Hake")## Warning in plot.gam(mod22, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod22)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 27 iterations.
## The RMS GCV score gradient at convergence was 1.692964e-06 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 1.58 1.20 0.9
## s(bt) 9.00 3.70 1.18 0.9## Goosefish
### GLM
dc_fall_g <- filter(dc_fall, svspp == 'Goosefish')
g_plot_f <- ggplot(data = dc_fall_g, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Goosefish")
g_plot_f
### GAM
dc_fall_g <- filter(dc_fall, svspp == 'Goosefish')
dc_fall_g$z_bt <- (dc_fall_g$bt - mean(dc_fall_g$bt))/(dc_fall_g$bt_sd)
mod23 <- gam(cb ~ s(year) + s(z_bt), data=dc_fall_g, family = 'gaussian')
summary(mod23)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(z_bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4648 0.1111 13.18 5.47e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 6.559 7.619 1.782 0.146
## s(z_bt) 6.555 7.589 1.955 0.103
##
## R-sq.(adj) = 0.479 Deviance explained = 63.8%
## GCV = 0.79972 Scale est. = 0.54319 n = 44mod23$aic## [1] 111.2226plot(mod23, pages=1, scheme=1, unconditional=TRUE, main = "Goosefish")## Warning in plot.gam(mod23, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod23)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 10 iterations.
## The RMS GCV score gradient at convergence was 4.770115e-05 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 6.56 1.05 0.61
## s(z_bt) 9.00 6.55 1.30 0.98## Thorny Skate
### GLM
dc_fall_t.s <- filter(dc_fall, svspp == 'Thorny.Skate')
t.s_plot_f <- ggplot(data = dc_fall_t.s, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Thorny Skate")
t.s_plot_f
### GAM
dc_fall_t.s <- filter(dc_fall, svspp == 'Thorny.Skate')
dc_fall_t.s$z_bt <- (dc_fall_t.s$bt - mean(dc_fall_t.s$bt))/(dc_fall_t.s$bt_sd)
mod24 <- gam(cb ~ s(year) + s(bt), data=dc_fall_t.s, family = 'gaussian')
summary(mod24)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.25542 0.02414 10.58 2.03e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 3.991 4.901 2.006 0.115
## s(bt) 1.204 1.375 0.149 0.884
##
## R-sq.(adj) = 0.159 Deviance explained = 26.8%
## GCV = 0.028143 Scale est. = 0.02389 n = 41mod24$aic## [1] -29.07856plot(mod24, pages=1, scheme=1, unconditional=TRUE, main = "Thorny Skate")## Warning in plot.gam(mod24, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod24)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 9 iterations.
## The RMS GCV score gradient at convergence was 8.809478e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 3.99 1.3 0.95
## s(bt) 9.00 1.20 1.1 0.70## Atlantic Cod
### GLM
dc_fall_a.c <- filter(dc_fall, svspp == 'Atlantic.Cod')
a.c_plot_f <- ggplot(data = dc_fall_a.c, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Atlanic Cod")
a.c_plot_f
### GAM
dc_fall_a.c <- filter(dc_fall, svspp == 'Atlantic.Cod')
dc_fall_a.c$z_bt <- (dc_fall_a.c$bt - mean(dc_fall_a.c$bt))/(dc_fall_a.c$bt_sd)
mod25 <- gam(cb ~ s(year) + s(bt), data=dc_fall_a.c, family = 'gaussian')
summary(mod25)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.50170 0.03426 14.64 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1.585 1.979 2.123 0.160
## s(bt) 1.000 1.000 0.752 0.391
##
## R-sq.(adj) = 0.0514 Deviance explained = 10.5%
## GCV = 0.059709 Scale est. = 0.055155 n = 47mod25$aic## [1] 2.633083plot(mod25, pages=1, scheme=1, unconditional=TRUE, main = "Atlantic Cod")## Warning in plot.gam(mod25, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod25)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 14 iterations.
## The RMS GCV score gradient at convergence was 6.263954e-08 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 1.58 1.21 0.83
## s(bt) 9.00 1.00 1.05 0.57## Sea Raven
### GLM
dc_fall_s.r <- filter(dc_fall, svspp == 'Sea.Raven')
s.r_plot_f <- ggplot(data = dc_fall_s.r, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Sea Raven")
s.r_plot_f
### GAM
dc_fall_s.r <- filter(dc_fall, svspp == 'Sea.Raven')
dc_fall_s.r$z_bt <- (dc_fall_s.r$bt - mean(dc_fall_s.r$bt))/(dc_fall_s.r$bt_sd)
mod26 <- gam(cb ~ s(year) + s(z_bt), data=dc_fall_s.r, family = 'gaussian')
summary(mod26)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(z_bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0002 0.1552 6.447 1.4e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1.000 1.000 5.424 0.0253 *
## s(z_bt) 1.049 1.096 1.052 0.3349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0802 Deviance explained = 12.7%
## GCV = 1.0663 Scale est. = 0.98701 n = 41mod26$aic## [1] 120.7466plot(mod26, pages=1, scheme=1, unconditional=TRUE, main = "Sea Raven")## Warning in plot.gam(mod26, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod26)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 12 iterations.
## The RMS GCV score gradient at convergence was 1.747061e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 1.00 1.42 1.00
## s(z_bt) 9.00 1.05 1.34 0.98## Silver Hake
### GLM
dc_fall_s.h <- filter(dc_fall, svspp == 'Silver.Hake')
s.h_plot_f <- ggplot(data = dc_fall_s.h, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Silver Hake")
s.h_plot_f
### GAM
dc_fall_s.h <- filter(dc_fall, svspp == 'Silver.Hake')
dc_fall_s.h$z_bt <- (dc_fall_s.h$bt - mean(dc_fall_s.h$bt))/(dc_fall_s.h$bt_sd)
mod27 <- gam(cb ~ s(year) + s(z_bt), data=dc_fall_s.h, family = 'gaussian')
summary(mod27)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(z_bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.5343 0.1173 13.08 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1.000 1.000 1.285 0.263
## s(z_bt) 2.643 3.264 2.149 0.108
##
## R-sq.(adj) = 0.103 Deviance explained = 17.2%
## GCV = 0.73148 Scale est. = 0.66072 n = 48mod27$aic## [1] 122.7285plot(mod27, pages=1, scheme=1, unconditional=TRUE, main = "Silver Hake")## Warning in plot.gam(mod27, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod27)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 11 iterations.
## The RMS GCV score gradient at convergence was 2.171858e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 1.00 1.31 0.96
## s(z_bt) 9.00 2.64 0.97 0.39## Summer Flounder
### GLM
dc_fall_s.f <- filter(dc_fall, svspp == 'Summer.Flounder')
s.f_plot_f <- ggplot(data = dc_fall_s.f, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Summer Flounder")
s.f_plot_f
### GAM
dc_fall_s.f <- filter(dc_fall, svspp == 'Summer.Flounder')
dc_fall_s.f$z_bt <- (dc_fall_s.f$bt - mean(dc_fall_s.f$bt))/(dc_fall_s.f$bt_sd)
mod28 <- gam(cb ~ s(year) + s(bt), data=dc_fall_s.f, family = 'gaussian')
summary(mod28)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.44691 0.03846 11.62 4.13e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1.000 1.000 1.329 0.25751
## s(bt) 8.509 8.926 3.167 0.00766 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.382 Deviance explained = 52.2%
## GCV = 0.084192 Scale est. = 0.063617 n = 43mod28$aic## [1] 14.53652plot(mod28, pages=1, scheme=1, unconditional=TRUE, main = "Summer Flounder")## Warning in plot.gam(mod28, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod28)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 12 iterations.
## The RMS GCV score gradient at convergence was 1.099816e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 1.00 1.16 0.86
## s(bt) 9.00 8.51 1.22 0.92## Fourspot Flounder
### GLM
dc_fall_f.f <- filter(dc_fall, svspp == 'Fourspot.Flounder')
f.f_plot_f <- ggplot(data = dc_fall_s.f, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Fourspot Flounder")
f.f_plot_f
### GAM
dc_fall_f.f <- filter(dc_fall, svspp == 'Fourspot.Flounder')
dc_fall_f.f$z_bt <- (dc_fall_f.f$bt - mean(dc_fall_f.f$bt))/(dc_fall_f.f$bt_sd)
mod29 <- gam(cb ~ s(year) + s(z_bt), data=dc_fall_f.f, family = 'gaussian')
summary(mod29)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(z_bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.61381 0.03907 15.71 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 4.041 4.958 2.861 0.0302 *
## s(z_bt) 5.601 6.659 1.858 0.1090
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.422 Deviance explained = 54.6%
## GCV = 0.091329 Scale est. = 0.070201 n = 46mod29$aic## [1] 19.52844plot(mod29, pages=1, scheme=1, unconditional=TRUE, main = "Fourspot Flounder")## Warning in plot.gam(mod29, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod29)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 7 iterations.
## The RMS GCV score gradient at convergence was 4.048242e-06 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 4.04 1.05 0.58
## s(z_bt) 9.00 5.60 1.18 0.88## Windowpane
### GLM
dc_fall_w <- filter(dc_fall, svspp == 'Windowpane')
w_plot_f <- ggplot(data = dc_fall_w, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Windowpane")
w_plot_f
### GAM
dc_fall_w <- filter(dc_fall, svspp == 'Windowpane')
dc_fall_w$z_bt <- (dc_fall_w$bt - mean(dc_fall_w$bt))/(dc_fall_w$bt_sd)
mod30 <- gam(cb ~ s(year) + s(z_bt), data=dc_fall_w, family = 'gaussian')
summary(mod30)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(z_bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7419 0.1118 6.636 1.59e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 6.709 7.827 1.875 0.0983 .
## s(z_bt) 1.692 2.117 1.699 0.2058
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.242 Deviance explained = 39.8%
## GCV = 0.67639 Scale est. = 0.525 n = 42mod30$aic## [1] 102.287plot(mod30, pages=1, scheme=1, unconditional=TRUE, main = "Windowpane")## Warning in plot.gam(mod30, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod30)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 10 iterations.
## The RMS GCV score gradient at convergence was 7.4314e-07 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 6.71 1.43 0.99
## s(z_bt) 9.00 1.69 1.17 0.87## Buckler Dory
### GLM
dc_fall_b.d <- filter(dc_fall, svspp == 'Buckler.Dory')
b.d_plot_f <- ggplot(data = dc_fall_b.d, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Buckler Dory")
b.d_plot_f## N < 30
### GAM
dc_fall_b.d <- filter(dc_fall, svspp == 'Buckler.Dory')
dc_fall_b.d$z_bt <- (dc_fall_b.d$bt - mean(dc_fall_b.d$bt))/(dc_fall_b.d$bt_sd)
mod31 <- gam(cb ~ s(year), data=dc_fall_b.d, family = 'gaussian')
mod31.a <- gam(cb ~ s(z_bt), data=dc_fall_b.d, family = 'gaussian')
summary(mod31)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7138 0.2177 3.278 0.007 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1.552 1.912 0.671 0.456
##
## R-sq.(adj) = 0.0579 Deviance explained = 17%
## GCV = 0.81158 Scale est. = 0.66365 n = 14summary(mod31.a)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(z_bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7138 0.2148 3.323 0.00608 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(z_bt) 1 1 2.176 0.166
##
## R-sq.(adj) = 0.083 Deviance explained = 15.4%
## GCV = 0.75367 Scale est. = 0.646 n = 14mod31$aic## [1] 38.27672mod31.a$aic## [1] 37.45481plot(mod31, pages=1, scheme=1, unconditional=TRUE, main = "Buckler Dory")## Warning in plot.gam(mod31, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
plot(mod31.a, pages=1, scheme=1, unconditional=TRUE, main = "Buckler Dory")## Warning in plot.gam(mod31.a, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod14)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 4 iterations.
## The RMS GCV score gradient at convergence was 2.394827e-06 .
## The Hessian was positive definite.
## Model rank = 10 / 10
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(bt) 9.00 3.58 1.53 0.99gam.check(mod14.a)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 4 iterations.
## The RMS GCV score gradient at convergence was 2.394827e-06 .
## The Hessian was positive definite.
## Model rank = 10 / 10
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(bt) 9.00 3.58 1.53 0.99## Blue Fish
### GLM
dc_fall_b.f <- filter(dc_fall, svspp == 'Buckler.Dory')
b.f_plot_f <- ggplot(data = dc_fall_b.f, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Blue Fish")
b.f_plot_f## N < 30
### GAM
dc_fall_b.f <- filter(dc_fall, svspp == 'Blue.Fish')
dc_fall_b.f$z_bt <- (dc_fall_b.f$bt - mean(dc_fall_b.f$bt))/(dc_fall_b.f$bt_sd)
mod32 <- gam(cb ~ s(year), data=dc_fall_b.f, family = 'gaussian')
mod32.a <- gam(cb ~ s(z_bt), data=dc_fall_b.f, family = 'gaussian')
summary(mod32)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0768 0.1418 7.593 2.43e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1 1 2.708 0.107
##
## R-sq.(adj) = 0.0391 Deviance explained = 6.2%
## GCV = 0.90701 Scale est. = 0.86482 n = 43summary(mod32.a)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(z_bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.077 0.145 7.424 4.44e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(z_bt) 1.397 1.676 0.447 0.706
##
## R-sq.(adj) = -0.00521 Deviance explained = 2.82%
## GCV = 0.95808 Scale est. = 0.90468 n = 43mod32$aic## [1] 119.7357mod32.a$aic## [1] 122.0486plot(mod32, pages=1, scheme=1, unconditional=TRUE, main = "Blue Fish")## Warning in plot.gam(mod32, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
plot(mod32.a, pages=1, scheme=1, unconditional=TRUE, main = "Blue Fish")## Warning in plot.gam(mod32.a, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod32)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 13 iterations.
## The RMS GCV score gradient at convergence was 1.442805e-07 .
## The Hessian was positive definite.
## Model rank = 10 / 10
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9 1 0.88 0.14gam.check(mod32.a)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 5 iterations.
## The RMS GCV score gradient at convergence was 5.428816e-06 .
## The Hessian was positive definite.
## Model rank = 10 / 10
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(z_bt) 9.0 1.4 1.04 0.46## Longhorn Sculpin
### GLM
dc_fall_l.s <- filter(dc_fall, svspp == 'Longhorn.Sculpin')
l.s_plot_f <- ggplot(data = dc_fall_l.s, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Longhorn Sculpin")
l.s_plot_f
### GAM
dc_fall_l.s <- filter(dc_fall, svspp == 'Longhorn.Sculpin')
dc_fall_l.s$z_bt <- (dc_fall_l.s$bt - mean(dc_fall_l.s$bt))/(dc_fall_l.s$bt_sd)
mod33 <- gam(cb ~ s(year) + s(z_bt), data=dc_fall_l.s, family = 'gaussian')
summary(mod33)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year) + s(z_bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.42171 0.04551 9.267 1.24e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 3.345 4.087 1.846 0.1402
## s(z_bt) 3.212 3.795 2.178 0.0942 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.236 Deviance explained = 36.5%
## GCV = 0.10213 Scale est. = 0.082835 n = 40mod33$aic## [1] 22.61746plot(mod33, pages=1, scheme=1, unconditional=TRUE, main = "Longhorn Sculpin")## Warning in plot.gam(mod33, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod33)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 4 iterations.
## The RMS GCV score gradient at convergence was 5.262473e-06 .
## The Hessian was positive definite.
## Model rank = 19 / 19
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 3.35 1.04 0.54
## s(z_bt) 9.00 3.21 1.06 0.52## Striped Searobin
### GLM
dc_fall_s.s <- filter(dc_fall, svspp == 'Striped.Searobin')
s.s_plot_f <- ggplot(data = dc_fall_s.s, aes(x = year, y = cb)) +
geom_point(color = "black", size = 1) +
ecodata::geom_gls() +
theme_bw() +
theme(plot.title = element_text(size = 13)) +
xlab("Year") +
ylab("Consumption:Biomass") +
scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) +
scale_y_continuous(expand = expansion(mult = c(0.02, 0.02))) +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 10),
axis.title.x=element_text(size = 10)) +
ggtitle("Striped Searobin")
s.s_plot_f## N < 30
### GAM
dc_fall_s.s <- filter(dc_fall, svspp == 'Striped.Searobin')
dc_fall_s.s$z_bt <- (dc_fall_s.s$bt - mean(dc_fall_s.s$bt))/(dc_fall_s.s$bt_sd)
mod34 <- gam(cb ~ s(year), data=dc_fall_s.s, family = 'gaussian')
mod34.a <- gam(cb ~ s(bt), data=dc_fall_s.s, family = 'gaussian')
summary(mod34)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(year)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0645 0.2227 9.271 4.9e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(year) 1.213 1.395 1.651 0.174
##
## R-sq.(adj) = 0.126 Deviance explained = 20.2%
## GCV = 0.87246 Scale est. = 0.74375 n = 15summary(mod34.a)##
## Family: gaussian
## Link function: identity
##
## Formula:
## cb ~ s(bt)
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.0645 0.1685 12.25 4.86e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(bt) 4.718 5.647 2.86 0.0803 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.5 Deviance explained = 66.8%
## GCV = 0.68832 Scale est. = 0.42592 n = 15mod34$aic## [1] 42.159mod34.a$aic## [1] 36.00201plot(mod34, pages=1, scheme=1, unconditional=TRUE, main = "Striped Searobin")## Warning in plot.gam(mod34, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
plot(mod34.a, pages=1, scheme=1, unconditional=TRUE, main = "Striped Searobin")## Warning in plot.gam(mod34.a, pages = 1, scheme = 1, unconditional = TRUE, :
## Smoothness uncertainty corrected covariance not available
gam.check(mod34)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 7 iterations.
## The RMS GCV score gradient at convergence was 4.696964e-06 .
## The Hessian was positive definite.
## Model rank = 10 / 10
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(year) 9.00 1.21 1.32 0.84gam.check(mod34.a)
##
## Method: GCV Optimizer: magic
## Smoothing parameter selection converged after 6 iterations.
## The RMS GCV score gradient at convergence was 1.212768e-07 .
## The Hessian was positive definite.
## Model rank = 10 / 10
##
## Basis dimension (k) checking results. Low p-value (k-index<1) may
## indicate that k is too low, especially if edf is close to k'.
##
## k' edf k-index p-value
## s(bt) 9.00 4.72 1.37 0.83Combining all of the above GLMs into one figure for comparison purposes:
glm_cb_f <- ggarrange(s.d_plot_f, w.s_plot_f, p_plot_f, w.h_plot_f, r.h_plot_f,g_plot_f, t.s_plot_f, a.c_plot_f, s.r_plot_f, s.h_plot_f,s.f_plot_f, f.f_plot_f, w_plot_f, b.d_plot_f, b.f_plot_f,l.s_plot_f, s.s_plot_f, ncol = 5, nrow = 4)## N < 30
## N < 30
## N < 30glm_cb_f
Comparing GAMs side by side for all species of trends in consumption:biomass ratios: (*Same note as the above combined set of GAMs)
par(mfrow=c(3,3))
mod18 <- gam(cb ~ s(year), data=dc_fall_s.d, family = 'gaussian')
plot(mod18, scheme=1, unconditional=TRUE, main = "Spiny Dogfish") ## Warning in plot.gam(mod18, scheme = 1, unconditional = TRUE, main = "Spiny
## Dogfish"): Smoothness uncertainty corrected covariance not availablemod19 <- gam(cb ~ s(year), data=dc_fall_w.s, family = 'gaussian')
plot(mod19, scheme=1, unconditional=TRUE, main = "Winter Skate") ## Warning in plot.gam(mod19, scheme = 1, unconditional = TRUE, main = "Winter
## Skate"): Smoothness uncertainty corrected covariance not availablemod20 <- gam(cb ~ s(year), data=dc_fall_p, family = 'gaussian')
plot(mod20, scheme=1, unconditional=TRUE, main = "Pollock") ## Warning in plot.gam(mod20, scheme = 1, unconditional = TRUE, main = "Pollock"):
## Smoothness uncertainty corrected covariance not availablemod21 <- gam(cb ~ s(year), data=dc_fall_w.h, family = 'gaussian')
plot(mod21, scheme=1, unconditional=TRUE, main = "White Hake")## Warning in plot.gam(mod21, scheme = 1, unconditional = TRUE, main = "White
## Hake"): Smoothness uncertainty corrected covariance not availablemod22 <- gam(cb ~ s(year), data=dc_fall_r.h, family = 'gaussian')
plot(mod22, scheme=1, unconditional=TRUE, main = "Red Hake")## Warning in plot.gam(mod22, scheme = 1, unconditional = TRUE, main = "Red Hake"):
## Smoothness uncertainty corrected covariance not availablemod23 <- gam(cb ~ s(year), data=dc_fall_g, family = 'gaussian')
plot(mod23, scheme=1, unconditional=TRUE, main = "Goosefish")## Warning in plot.gam(mod23, scheme = 1, unconditional = TRUE, main =
## "Goosefish"): Smoothness uncertainty corrected covariance not availablemod24 <- gam(cb ~ s(year), data=dc_fall_t.s, family = 'gaussian')
plot(mod24, scheme=1, unconditional=TRUE, main = "Thorny Skate")## Warning in plot.gam(mod24, scheme = 1, unconditional = TRUE, main = "Thorny
## Skate"): Smoothness uncertainty corrected covariance not availablemod25 <- gam(cb ~ s(year), data=dc_fall_a.c, family = 'gaussian')
plot(mod25, scheme=1, unconditional=TRUE, main = "Atlantic Cod")## Warning in plot.gam(mod25, scheme = 1, unconditional = TRUE, main = "Atlantic
## Cod"): Smoothness uncertainty corrected covariance not availablemod26 <- gam(cb ~ s(year), data=dc_fall_s.r, family = 'gaussian')
plot(mod26, scheme=1, unconditional=TRUE, main = "Sea Raven")## Warning in plot.gam(mod26, scheme = 1, unconditional = TRUE, main = "Sea
## Raven"): Smoothness uncertainty corrected covariance not available
mod27 <- gam(cb ~ s(year), data=dc_fall_s.h, family = 'gaussian')
plot(mod27, scheme=1, unconditional=TRUE, main = "Silver Hake")## Warning in plot.gam(mod27, scheme = 1, unconditional = TRUE, main = "Silver
## Hake"): Smoothness uncertainty corrected covariance not availablemod28 <- gam(cb ~ s(year), data=dc_fall_s.f, family = 'gaussian')
plot(mod28, scheme=1, unconditional=TRUE, main = "Summer Flounder")## Warning in plot.gam(mod28, scheme = 1, unconditional = TRUE, main = "Summer
## Flounder"): Smoothness uncertainty corrected covariance not availablemod29 <- gam(cb ~ s(year), data=dc_fall_f.f, family = 'gaussian')
plot(mod29, scheme=1, unconditional=TRUE, main = "Fourspot Flounder")## Warning in plot.gam(mod29, scheme = 1, unconditional = TRUE, main = "Fourspot
## Flounder"): Smoothness uncertainty corrected covariance not availablemod30 <- gam(cb ~ s(year), data=dc_fall_w, family = 'gaussian')
plot(mod30, scheme=1, unconditional=TRUE, main = "Windowpane")## Warning in plot.gam(mod30, scheme = 1, unconditional = TRUE, main =
## "Windowpane"): Smoothness uncertainty corrected covariance not availablemod31 <- gam(cb ~ s(year), data=dc_fall_b.d, family = 'gaussian')
plot(mod31, scheme=1, unconditional=TRUE, main = "Buckler Dory")## Warning in plot.gam(mod31, scheme = 1, unconditional = TRUE, main = "Buckler
## Dory"): Smoothness uncertainty corrected covariance not availablemod32 <- gam(cb ~ s(year), data=dc_fall_b.f, family = 'gaussian')
plot(mod32, scheme=1, unconditional=TRUE, main = "Blue Fish")## Warning in plot.gam(mod32, scheme = 1, unconditional = TRUE, main = "Blue
## Fish"): Smoothness uncertainty corrected covariance not availablemod33 <- gam(cb ~ s(year), data=dc_fall_l.s, family = 'gaussian')
plot(mod33, scheme=1, unconditional=TRUE, main = "Longhorn Sculpin")## Warning in plot.gam(mod33, scheme = 1, unconditional = TRUE, main = "Longhorn
## Sculpin"): Smoothness uncertainty corrected covariance not availablemod34 <- gam(cb ~ s(year), data=dc_fall_s.s, family = 'gaussian')
plot(mod34, scheme = 1, unconditional=TRUE, main = "Striped Searobin")## Warning in plot.gam(mod34, scheme = 1, unconditional = TRUE, main = "Striped
## Searobin"): Smoothness uncertainty corrected covariance not available## GAM of bt v. cb --> same note as above
par(mfrow = c(3,3))
mod18 <- gam(cb ~ s(bt), data=dc_spring_s.d, family = 'gaussian')
plot(mod18, scheme=1, unconditional=TRUE, main = "Spiny Dogfish")## Warning in plot.gam(mod18, scheme = 1, unconditional = TRUE, main = "Spiny
## Dogfish"): Smoothness uncertainty corrected covariance not availablemod19 <- gam(cb ~ s(bt), data=dc_spring_w.s, family = 'gaussian')
plot(mod19, scheme=1, unconditional=TRUE, main = "Winter Skate")## Warning in plot.gam(mod19, scheme = 1, unconditional = TRUE, main = "Winter
## Skate"): Smoothness uncertainty corrected covariance not availablemod20 <- gam(cb ~ s(bt), data=dc_spring_p, family = 'gaussian')
plot(mod20, scheme=1, unconditional=TRUE, main = "Pollock")## Warning in plot.gam(mod20, scheme = 1, unconditional = TRUE, main = "Pollock"):
## Smoothness uncertainty corrected covariance not availablemod21 <- gam(cb ~ s(bt), data=dc_spring_w.h, family = 'gaussian')
plot(mod21, scheme=1, unconditional=TRUE, main = "White Hake")## Warning in plot.gam(mod21, scheme = 1, unconditional = TRUE, main = "White
## Hake"): Smoothness uncertainty corrected covariance not availablemod22 <- gam(cb ~ s(bt), data=dc_spring_r.h, family = 'gaussian')
plot(mod22, scheme=1, unconditional=TRUE, main = " Red Hake")## Warning in plot.gam(mod22, scheme = 1, unconditional = TRUE, main = " Red
## Hake"): Smoothness uncertainty corrected covariance not availablemod23 <- gam(cb ~ s(bt), data=dc_spring_g, family = 'gaussian')
plot(mod23, scheme=1, unconditional=TRUE, main = "Goosefish")## Warning in plot.gam(mod23, scheme = 1, unconditional = TRUE, main =
## "Goosefish"): Smoothness uncertainty corrected covariance not availablemod24 <- gam(cb ~ s(bt), data=dc_spring_t.s, family = 'gaussian')
plot(mod24, scheme=1, unconditional=TRUE, main = "Thorny Skate")## Warning in plot.gam(mod24, scheme = 1, unconditional = TRUE, main = "Thorny
## Skate"): Smoothness uncertainty corrected covariance not availablemod25 <- gam(cb ~ s(bt), data=dc_spring_a.c, family = 'gaussian')
plot(mod25, scheme=1, unconditional=TRUE, main = "Atlantic Cod")## Warning in plot.gam(mod25, scheme = 1, unconditional = TRUE, main = "Atlantic
## Cod"): Smoothness uncertainty corrected covariance not availablemod26 <- gam(cb ~ s(bt), data=dc_spring_s.r, family = 'gaussian')
plot(mod26, scheme=1, unconditional=TRUE, main = "Sea Raven")## Warning in plot.gam(mod26, scheme = 1, unconditional = TRUE, main = "Sea
## Raven"): Smoothness uncertainty corrected covariance not available
mod27 <- gam(cb ~ s(bt), data=dc_spring_s.h, family = 'gaussian')
plot(mod27, scheme=1, unconditional=TRUE, main = "Silver Hake")## Warning in plot.gam(mod27, scheme = 1, unconditional = TRUE, main = "Silver
## Hake"): Smoothness uncertainty corrected covariance not availablemod28 <- gam(cb ~ s(bt), data=dc_spring_s.f, family = 'gaussian')
plot(mod28, scheme=1, unconditional=TRUE, main = "Summer Flounder")## Warning in plot.gam(mod28, scheme = 1, unconditional = TRUE, main = "Summer
## Flounder"): Smoothness uncertainty corrected covariance not availablemod29 <- gam(cb ~ s(bt), data=dc_spring_f.f, family = 'gaussian')
plot(mod29, scheme=1, unconditional=TRUE, main = "Fourspot Flounder")## Warning in plot.gam(mod29, scheme = 1, unconditional = TRUE, main = "Fourspot
## Flounder"): Smoothness uncertainty corrected covariance not availablemod30 <- gam(cb ~ s(bt), data=dc_spring_w, family = 'gaussian')
plot(mod30, scheme=1, unconditional=TRUE, main = "Windowpane")## Warning in plot.gam(mod30, scheme = 1, unconditional = TRUE, main =
## "Windowpane"): Smoothness uncertainty corrected covariance not availablemod31 <- gam(cb ~ s(bt) , data=dc_spring_b.d, family = 'gaussian')
plot(mod31, scheme=1, unconditional=TRUE, main = "Buckler Dory")## Warning in plot.gam(mod31, scheme = 1, unconditional = TRUE, main = "Buckler
## Dory"): Smoothness uncertainty corrected covariance not availablemod32 <- gam(cb ~ s(bt), data=dc_spring_b.f, family = 'gaussian')
plot(mod32, scheme=1, unconditional=TRUE, main = "Blue Fish")## Warning in plot.gam(mod32, scheme = 1, unconditional = TRUE, main = "Blue
## Fish"): Smoothness uncertainty corrected covariance not availablemod33 <- gam(cb ~ s(z_bt), data=dc_spring_l.s, family = 'gaussian')
plot(mod33, scheme=1, unconditional=TRUE, main = "Longhorn Sculpin")## Warning in plot.gam(mod33, scheme = 1, unconditional = TRUE, main = "Longhorn
## Sculpin"): Smoothness uncertainty corrected covariance not availablemod34 <- gam(cb ~ s(z_bt), data=dc_spring_s.s, family = 'gaussian')
plot(mod34, scheme = 1, unconditional=TRUE, main = "Striped Searobin")## Warning in plot.gam(mod34, scheme = 1, unconditional = TRUE, main = "Striped
## Searobin"): Smoothness uncertainty corrected covariance not available
Filtering data to examine bottom temperature trend:
dc_temp <- data_comb %>%
select('year','bt') %>%
na.omit('year','bt')Bottom temperature trend across spring and fall:
bt_plot <- ggplot(data = dc_temp,
aes(x = year, y = bt)) +
geom_point(color = "black", size = 1) +
geom_smooth(method = lm, color = "purple2", linewidth = 2) +
theme_bw() +
theme(plot.title = element_text(size = 15)) +
xlab("Year") +
ylab("Bottom Temperature") +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 13),
axis.title.x=element_text(size = 13)) +
ggtitle("Bottom Temperature Over Time")
bt_plot## `geom_smooth()` using formula = 'y ~ x'
Bottom temperature trend across fall:
bt_fall_plot <- ggplot(data = dc_fall,
aes(x = year, y = bt)) +
geom_point(color = "black", size = 1) +
geom_smooth(method = lm, color = "purple2", linewidth = 2, se = FALSE) +
theme_bw() +
theme(plot.title = element_text(size = 15)) +
xlab("Year") +
ylab("Bottom Temperature") +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 13),
axis.title.x=element_text(size = 13)) +
ggtitle("Fall Bottom Temperature Over Time")
bt_fall_plot## `geom_smooth()` using formula = 'y ~ x'
Bottom temperature trend across spring:
dc_spring <- filter(data_comb_f, seacat == 'SPRING')
bt_spring_plot <- ggplot(data = dc_spring,
aes(x = year, y = bt)) +
geom_point(color = "black", size = 1) +
geom_smooth(method = lm, color = "purple2", linewidth = 2, se = FALSE) +
theme_bw() +
theme(plot.title = element_text(size = 15)) +
xlab("Year") +
ylab("Bottom Temperature") +
theme(axis.text.x=element_text(size=10),
axis.text.y = element_text(size=10),
axis.title.y=element_text(size = 13),
axis.title.x=element_text(size = 13)) +
ggtitle("Spring Bottom Temperature Over Time")
bt_spring_plot## `geom_smooth()` using formula = 'y ~ x'